Body Mass Index Is an Important Predictor for Suicide: Results from a Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Public health concerns for the independent management of obesity and suicidal behavior are rising. Emerging evidence suggests body weight plays an important role in quantifying the risk of suicide. In light of these findings, we aimed to clarify the association between body mass index (BMI) and suicidal behavior by systematically reviewing and evaluating the literature. Studies were identified by searching MEDLINE, EMBASE, PsycINFO, and CINAHL from inception to January 2015, supplemented by hand and grey literature searches. Study screening, data extraction, and risk of bias assessment were conducted in duplicate. We included 38 observational studies. Meta-analyses supported an inverse association between BMI and completed suicide. Pooled summary estimates demonstrated that underweight was significantly associated with an increased risk of completed suicide (HR = 1.21, 95% CI 1.07 to 1.36, p = .002), and obesity (HR = 0.71, 95% CI 0.56 to 0.89, p = .003) and overweight (HR = 0.78, 95% CI 0.75 to 0.82, p < .0001) were significantly associated with a decreased risk of completed suicide relative to normal weight. A qualitative summary of the literature demonstrated conflicting evidence regarding the association between BMI and attempted suicide and revealed no association between BMI and suicidal ideation. BMI may be used to aid the assessment of suicide risk, especially that of completed suicide. However, unmeasured confounders and systematic biases of individual studies limit the quality of evidence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.038 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".